无人机反应式扰动流体路径规划

In this paper, aiming at complex 3D obstacle environments, a reactive interfered fluid path planning framework is proposed for unmanned aerial vehicles (UAV) based on deep reinforcement learning. The constrained interfered fluid dynamical system algorithm is used as the fundamental path planning method in the framework. According to relative states between unmanned aerial vehicles and each obstacle, and categories of obstacles, the reaction and direction coefficients of the corresponding obstacle are generated online using the actor networks trained by deep deterministic policy gradient. On this basis, the total modulation matrices in constrained interfered fluid dynamical system can be resolved and the flight path is accordingly modified to realize the reactive obstacle avoidance. In addition, the normative modeling method of deep reinforcement learning training environments, which is matched with the proposed path planning method, is studied. Finally, simulation results show that the proposed method is obviously superior to the online path planning method based on predictive control in real-time performance under the condition that the path qualities are approximately the same.